A Merging Framework for Rainfall Estimation at High Spatiotemporal Resolution for Distributed Hydrological Modeling in a Data-Scarce Area

Yinping Long, Yaonan Zhang, Qimin Ma
2016 Remote Sensing  
Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolution of satellite data are available. The objective of the study is to present a satellite and rain
more » ... tellite and rain gauge data-merging framework adapting for coarse resolution and data-sparse designs. In the framework, a statistical spatial downscaling method based on the relationships among precipitation, topographical features, and weather conditions was used to downscale the 0.25˝daily rainfall field derived from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) precipitation product version 7. The nonparametric merging technique of double kernel smoothing, adapting for data-sparse design, was combined with the global optimization method of shuffled complex evolution, to merge the downscaled TRMM and gauged rainfall with minimum cross-validation error. An indicator field representing the presence and absence of rainfall was generated using the indicator kriging technique and applied to the previously merged result to consider the spatial intermittency of daily rainfall. The framework was applied to estimate daily precipitation at a 1 km resolution in the Qinghai Lake Basin, a data-scarce area in the northeast of the Qinghai-Tibet Plateau. The final estimates not only captured the spatial pattern of daily and annual precipitation with a relatively small estimation error, but also performed very well in stream flow simulation when applied to force the geomorphology-based hydrological model (GBHM). The proposed framework thus appears feasible for rainfall estimation at high spatiotemporal resolution in data-scarce areas. Mapping rainfall is always challenging. Interpolation of point rainfall measured by rain gauges is the conventional method (for example, with Thiessen polygons, inverse distance weighting, or kriging techniques), but this may be subject to great uncertainty when the rain gauge network is sparse, as the gauges can only represent rainfall information within a limited distance [7] . In contrast, remote sensing techniques provide an evolutionary method of spatial continuous rainfall observation with a high temporal sampling frequency. However, remote sensing products may also generate major quantitative errors, due to cloud effects and limitations in remote sensor performance and retrieval algorithms [8] . Combining both data sources, known as data merging may, thus, be effective in maintaining both high-quality rainfall data from stations and spatially-continuous information from remote sensing observations [9] . Great efforts have been made to develop and evaluate algorithms for merging rain gauge and remote sensing observations, e.g., co-kriging [10,11], linearized weighting procedures [8, 12] , conditional merging [13], Barnes objective analysis [14] , multi-quadric surface fitting [9], and double kernel smoothing [15] . The outcomes of these studies are encouraging, and provide new methods of estimating spatiotemporal precipitation, particularly in areas with limited climate data, such as the Qinghai-Tibetan Plateau. However, it is quite a challenging job to generate high spatial and temporal rainfall maps for distributed hydrological modeling at a local basin scale when facing the following problems all at once: (1) only a sparse rain gauge network is available; (2) the spatial resolution of satellite data is much coarser than the modeled one [7]; and (3) daily rainfall is spatially intermittent [11] . This study thus aims to present a methodology that overcomes these issues. The kriging-based merging scheme is a common and mature spatial prediction method, but requires the assumption of a second-order stationary and a theoretical semi-variogram model. In poorly-gauged areas kriging-derived methods may, thus, overestimate the spatial correlation, as distances between rain gauges are often too large and, hence, tend to deliver unsatisfactory results [16] . Instead, nonparametric merging methods without strong spatial assumptions may be more suitable for sparse designs. Li and Shao [15] used the nonparametric double kernel smoothing technique to combine TRMM precipitation data with observations from the Australian rain gauge network, focusing on discontinuity correction and spatial interpolation adapting for sparse design, and compared this to the geostatistical methods of ordinary kriging and co-kriging. Nerini et al. [16] compared the nonparametric rainfall methods of double kernel smoothing and mean bias correction with two geostatistical methods-kriging with external drift and Bayesian combination-for merging daily TRMM precipitation with rain gauge data over a mesoscale tropical Andean catchment in Peru. Both studies concluded that the nonparametric double kernel smoothing merging method performed better than the more complex geostatistical methods under data-scarce conditions. Nonetheless, the precipitation with finer spatial resolution necessary for hydrological models is still not present, as the merged results often retain the same scale as the satellite data. Spatial downscaling of the satellite observations is thus necessary before the merging, which is a technique for disaggregating coarse-resolution data and capturing the sub-grid heterogeneity. It is usually based on the concept of scale invariance, or relates the properties of the physical process at one scale to those at a finer scale [17] . A common and simple downscaling method is to develop a statistical model at the original coarse scale, based on the relationships between rainfall and the main factors that govern the rainfall spatial variability, and then transfer the model to the target scale,
doi:10.3390/rs8070599 fatcat:i3uoahml5zhlbaeakhvuqwgu2q